/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2016 Soumyajit De
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 * The views and conclusions contained in the software and documentation are those
 * of the authors and should not be interpreted as representing official policies,
 * either expressed or implied, of the Shogun Development Team.
 */

#include <shogun/base/some.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/statistical_testing/internals/Kernel.h>
#include <gtest/gtest.h>

using namespace shogun;
using namespace internal;

TEST(SelfAdjointKernelFunctor, kernel)
{
	const index_t dim=3;
	const index_t num_vec=8;
	const float64_t sigma=0.1;

	SGMatrix<float64_t> data(dim, num_vec);
	for (auto i=0; i<dim*num_vec; ++i)
		data.matrix[i]=sg_rand->random(0.0, 0.1);
	auto feats=some<CDenseFeatures<float64_t> >(data);

	auto kernel=some<CGaussianKernel>(10, 2*sigma*sigma);
	kernel->init(feats, feats);

	SelfAdjointPrecomputedKernel kernel_functor;
	kernel_functor.precompute(kernel);

	for (auto i=0; i<num_vec; ++i)
	{
		for (auto j=0; j<num_vec; ++j)
			EXPECT_NEAR(kernel->kernel(i, j), kernel_functor(i, j), 1E-6);
	}
}
